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<table width="100%" summary="page for income"><tr><td>income</td><td align="right">R Documentation</td></tr></table>

<h2>US family income from US census 2008
</h2>

<h3>Description</h3>

<p>US census data on family income from 2008
</p>


<h3>Usage</h3>

<pre>data(income)</pre>


<h3>Format</h3>


<p>A data frame with 44 observations on the following 4 variables.
</p>

<dl>
<dt><code>value</code></dt><dd><p>lower boundary of the income group</p>
</dd>
<dt><code>count</code></dt><dd><p>Number of families within that income group</p>
</dd>
<dt><code>mean</code></dt><dd><p>Mean of the category</p>
</dd>
<dt><code>prop</code></dt><dd><p>proportion of families</p>
</dd>
</dl>



<h3>Details</h3>

<p>The distribution of income is a nice example of a log normal distribution.  It is also an interesting example of the power of graphics. It is quite clear when graphing the data that income statistics are bunched to the nearest 5K.  That is, there is a clear sawtooth pattern in the data.
</p>
<p>The all.income set is interpolates intervening values for 100-150K, 150-200K and 200-250K</p>


<h3>Source</h3>

<p>US Census: Table HINC-06. Income Distribution to $250,000 or More for Households: 2008
</p>
<p>http://www.census.gov/hhes/www/cpstables/032009/hhinc/new06_000.htm
</p>


<h3>Examples</h3>

<pre>
data(income)
with(income[1:40,], plot(mean,prop, main="US family income for 2008",xlab="income", ylab="Proportion of families",xlim=c(0,100000)))
with (income[1:40,], points(lowess(mean,prop,f=.3),typ="l"))
describe(income)


with(all.income, plot(mean,prop, main="US family income for 2008",xlab="income", ylab="Proportion of families",xlim=c(0,250000)))
with (all.income[1:50,], points(lowess(mean,prop,f=.25),typ="l"))
#curve(100000* dlnorm(x, 10.8, .8), x = c(0,250000),ylab="Proportion")
</pre>


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